Let's start by loading and preparing the California housing dataset.
We would:
first load the data
then split it into a training set, a validation set, and a test set
finally, we scale it.
Note that this dataset contains only numerical features and there are no missing values.
Note:
For a feature scaler like StandardScaler, fit computes the mean and std(standard deviation) to be used for later scaling (just a computation) based on the given data, nothing is given to you. transform uses a previously computed mean and std to autoscale the data (subtract mean from all values and then divide it by std). fit_transform does both at the same time.
So, we would be applying the scaler.fit_transform on the train data, and just apply scaler.transform on the validation and test data.
Import fetch_california_housing from sklearn.datasets.
from << your code comes here >> import << your code comes here >>
Use fetch_california_housing() we imported from sklearn.datasets to load the data.
housing = << your code comes here >>()
Import train_test_split from sklearn.model_selection.
from << your code comes here >> import << your code comes here >>
Split the data into a training set, a validation set, and a test set using train_test_split.
X_train_full, X_test, y_train_full, y_test = << your code comes here >>( housing.data,
housing.target.reshape(-1, 1), random_state=42)
X_train, X_valid, y_train, y_valid = << your code comes here >>( X_train_full,
y_train_full, random_state=42)
Import StandardScaler from sklearn.preprocessing.
from << your code comes here >> import << your code comes here >>
Use StandardScaler to get scaler.
scaler = << your code comes here >>()
Scale all the train, validation, and test features.
Use scaler.fit_transform on X_train and store it in X_train_scaled.
X_train_scaled = << your code comes here >>(X_train)
Use scaler.transform on X_valid and store it in X_valid_scaled.
X_valid_scaled = << your code comes here >>(X_valid)
Use scaler.transform on X_test and store it in X_test_scaled.
X_test_scaled = << your code comes here >>(X_test)
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